Methodology

Introduction

Following the completion of the pilot study in Autumn 2019 and the project restart in summer 2022, the fieldwork methodology was revised to maximise the efficient collection of representative, high-quality data.

This section describes the survey design, fieldwork and data collection approach, and approach to data analysis.

Survey Design

The four-part survey was divided as follows:

Part A – Local Trunk Road Focus

Respondents were first asked to indicate where on a map of their local area most problems occurred. For the section they indicated, they were asked about perceived vehicle speeds, time issues occur, road crossing conditions and walking and cycling conditions along the study area.

Part B – Barriers to Active Travel

In Part B, respondents were asked to indicate the degree to which a range of factors hindered their ability to walk or cycle in a) the study area and b) the wider area (20-minute walk / a mile). The following factors were chosen to be scored as Never, Rarely, Often or Always impacting their ability to walk or cycle:

• Speed of traffic
• Amount of traffic
• Lack of crossing points
• Signalised crossings not allowing adequate time to cross.
• Poor road lighting
• Poor footways
• Noise pollution
• Air pollution
• Fear for personal safety
• Lack of safe cycling infrastructure, hindering cycling

Part C – Resident Views

Part C was a free text question that allowed respondents to provide feedback and give their opinion on what improvements they would like to see in their local area to facilitate travel of any type around their local area.

Part D – About You

Part D asked the respondent general questions about themselves / their household to capture demographic information.

A copy of the survey is included in Appendix B.

FieldWork Approach

Surveys were conducted between September 2022 – November 2022, with two WSP team members visiting each of the settlements to distribute the surveys.

A combined method of in-person/freepost return was chosen for survey distribution, with surveyors introducing themselves and explaining the survey at 1:2 – 1:5 residences depending on population density.

The respondents were left to answer in their own time and could either return the survey to the surveyor later that day or by freepost return. At houses with no response or which were not targeted for in-person discussion, surveys were posted through the letterbox for freepost return.

Sampling

The following settlements were selected as the study areas, with surveys issued to all households living within 50m of the trunk road:

• Rosyth
• Methven
• Freuchie
• Keith
• Fort William
• Inverness
• Skelmorlie
• Lochearnhead
• Drumnadrochit
• Helmsdale
• Newtonmore
• Kincardine
• Mauchline

These settlements were selected based on the combined professional experience of WSP and Transport Scotland as reflective of a variety of trunk road residential contexts.

Response Rate

Across the 13 settlements that were included in the study, there were 589 responses, a response rate of 26%.

Quantitative Data Analysis

This section summarises the approach to quantitative data analysis of the surveys; the results of this analysis are presented in Section 4.

Determination of the primary Dependent Variable

A comparison between perceived and ATC-measured traffic speeds at the location which participants marked on the Part A map was used to address the primary research question, which focused on residents’ perception of speed.

The key dependent variable for the central research question was termed the ‘relative speed estimate’, calculated as:

Measured 85th percentile traffic speed – Estimated traffic speed =
Relative Speed Estimate

As estimated traffic speed was recorded in bands (e.g., 30-35 mph), the maximum speed of the band was used for the above calculation.

Descriptive Statistics

The sample was analysed along the demographic measures described in Table 3-1.

Table 3-1 – Demographic Characteristics and Coding

Demographic Characteristic	Response Options
Gender	Female, Male, Other, Prefer Not to Say
Age	In bands from 18-24 to 85+
Number of years living in residence	Free response
Household description	Retired, Immediate Family, Multi-Occupancy (related), Multi-Occupancy (not related), Single-Occupancy, Couple, Prefer Not to Say
Mobility-Limiting Disability	Yes / No
Household vehicle ownership	None, One, Two or More
Frequency of contact with neighbours	Three or more times a week, Once or twice a week, Once or twice a month, Less often or never
Pet ownership	Yes / No, free response to indicate type and number

Due to the geographically-targeted nature of the analysis, the sample was not assessed or weighted against population-wide demographics.

Demographic Analysis

The analysis team used two sample t-tests for unequal variances, conducted in Excel, to compare the mean relative speed estimates between the following demographic groups:

• Male and female respondents
• Disabled and non-disabled respondents
• Respondents with and without a vehicle in the household
• Respondents with and without children in the household
• Respondents with and without a dog and/or cat in the household

Additionally, a linear regression model was used to assess the relationship between age and relative speed estimate.

Coding of Independent Variables

Table 3-2 lists the variables included in the regression models and shows how categorical variables were coded for inclusion in linear and logistic regression modelling: 

Table 3-2 – Independent Variables, Data Source and Numerical Assignment

Demographic Characteristic	Data Source	Coding
Gender	Survey	Female (2), Male (1), Other (3), Prefer Not to Say (0)
Age	Survey	In bands from 18-24 to 85+ - youngest age in band used for regression
Number of years living in residence	Survey	Numerical data (years)
Household description	Survey	Retired, Immediate Family, Multi-Occupancy (related), Multi-Occupancy (not related), Single-Occupancy, Couple, Prefer Not to Say
Mobility-Limiting Disability	Survey	Yes (1) / No (0)
Household vehicle ownership	Survey	None (0), One (1), Two or More (2)
Frequency of contact with neighbours	Survey	Three or more times a week (3), Once or twice a week (2), Once or twice a month (1), Less often or never (0)
Pet ownership	Survey	Dog and / or Cat: Yes (1) / No (0)
Width of Road	Google Maps	Estimated width in metres
85th Percentile Speed	ATC	Speed in miles per hour
Volume of Traffic (10k's)	ATC	7-day daily average count / 10,000
Land Use	Google Maps	Commercial (3), Mixed Use (2), Residential (1), Unoccupied (0)
Barrier between footway and traffic	Google Maps	Most of carriageway section (as indicated on map) (2), Some of carriageway section (1), None (0)
Presence of bus stop(s)	Google Maps	One or more bus stop located in carriageway section indicated on the map: Yes (1), No (0)
Pedestrian crossing in road section?	Google Maps	Pedestrian Island (1), Zebra (2), Controlled (3), None (0)
Footway width	Google Maps	Estimated width in metres
School near the section?	Google Maps	School within 100m of road section: yes (1) / no (0)
Footway	Google Maps	Both sides of road section (2), One side (1), None
Lanes of Traffic	Google Maps	Number of lanes (Note that this variable was removed from analysis as all considered road sections had two lanes.)

Model Construction

Three models were prepared using the XLMiner Analysis ToolPak in Excel:

• Multiple linear regression model: relationship between independent variables and relative speed estimate
• Multiple logistic regression model 1: relationship between independent variables and probability of 5+mph relative speed estimate
• Multiple logistic regression model 2: relationship between independent variables and 10+mph relative speed estimate

Model Fitting

The model was then fitted using non-automated backward elimination. The initial model was built including all available predictor (independent) variables. One by one, the variables with the highest p-value (level of statistical significance) were eliminated from the model. The process was repeated iteratively, with the analysis team removing variables until a model was obtained with all variables below the designated alpha value (i.e., p < 0.10). This manual approach allowed the model to be tailored to the specific problem and make informed decisions based on the data and our expertise.

Qualitative Data Analysis

For the ‘Your Views’ section of the survey (Part C), the project team manually coded each response into key categories and summarised the feedback in the Settlement-Specific Datasheets (Appendix A) with additional emphasis placed on more frequently occurring feedback.